# Copyright (c) 2025, Huawei Technologies Co., Ltd. All rights reserved.

from typing import Optional

import torch
import triton
import triton.language as tl

from mindspeed.lite.ops.triton.utils import prepare_chunk_indices


@triton.heuristics({
    'HAS_SCALE': lambda args: args['scale'] is not None,
    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None
})
@triton.autotune(
    configs=[
        triton.Config({}, num_warps=num_warps)
        for num_warps in [1, 2, 4, 8]
    ],
    key=['B', 'H', 'BT', 'IS_VARLEN', 'REVERSE']
)
@triton.jit(do_not_specialize=['T'])
def chunk_local_cumsum_scalar_kernel(
    s,
    o,
    scale,
    cu_seqlens,
    chunk_indices,
    T,
    B: tl.constexpr,
    H: tl.constexpr,
    BT: tl.constexpr,
    REVERSE: tl.constexpr,
    HAS_SCALE: tl.constexpr,
    IS_VARLEN: tl.constexpr,
    HEAD_FIRST: tl.constexpr,
):
    i_t, i_b = tl.program_id(0), tl.program_id(1)
    if IS_VARLEN:
        i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
        bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
        T = eos - bos
    else:
        bos, eos = i_b * T, i_b * T + T

    for i_h in range(0, H):
        if HEAD_FIRST:
            p_s = tl.make_block_ptr(s + bos * H + i_h * T, (T,), (1,), (i_t * BT,), (BT,), (0,))
            p_o = tl.make_block_ptr(o + bos * H + i_h * T, (T,), (1,), (i_t * BT,), (BT,), (0,))
        else:
            p_s = tl.make_block_ptr(s + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
            p_o = tl.make_block_ptr(o + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))

        b_s = tl.load(p_s, boundary_check=(0,)).to(tl.float32)
        b_o = tl.cumsum(b_s, axis=0)
        if REVERSE:
            b_z = tl.sum(b_s, axis=0)
            b_o = -b_o + b_z[None] + b_s
        if HAS_SCALE:
            b_o *= scale
        tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0,))


def chunk_local_cumsum_scalar(
    g: torch.Tensor,
    chunk_size: int,
    reverse: bool = False,
    scale: float = None,
    cu_seqlens: Optional[torch.Tensor] = None,
    head_first: bool = False,
    output_dtype: Optional[torch.dtype] = torch.float
) -> torch.Tensor:
    if head_first:
        g = g.transpose(1, 2).contiguous()
        B, H, T = g.shape
    else:
        B, T, H = g.shape
    if chunk_size != 2 ** (chunk_size.bit_length() - 1):
        raise ValueError(
            f"chunk_size must be a power of 2, chunk_size is{chunk_size}"
        )
    BT = chunk_size
    chunk_indices = prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None
    NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
    g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)
    grid = (NT, B)
    chunk_local_cumsum_scalar_kernel[grid](
        s=g_org,
        o=g,
        scale=scale,
        cu_seqlens=cu_seqlens,
        chunk_indices=chunk_indices,
        T=T,
        B=B,
        H=H,
        BT=BT,
        HEAD_FIRST=head_first,
        REVERSE=reverse
    )
    if head_first:
        g = g.transpose(1, 2).contiguous()
    return g


def chunk_local_cumsum(
    g: torch.Tensor,
    chunk_size: int,
    reverse: bool = False,
    scale: float = None,
    cu_seqlens: Optional[torch.Tensor] = None,
    head_first: bool = False,
    output_dtype: Optional[torch.dtype] = torch.float,
    **kwargs
) -> torch.Tensor:
    if cu_seqlens is not None:
        if g.shape[0] != 1:
            raise ValueError(
                f"Only batch size 1 is supported when cu_seqlens are provided, current size is{g.shape[0]}"
            )
    if len(g.shape) == 3:
        return chunk_local_cumsum_scalar(
            g=g,
            chunk_size=chunk_size,
            reverse=reverse,
            scale=scale,
            cu_seqlens=cu_seqlens,
            head_first=head_first,
            output_dtype=output_dtype
        )
    else:
        raise ValueError(
            f"Unsupported input shape {g.shape}, "
            f"which should be (B, T, H, D) if `head_first=False` "
            f"or (B, H, T, D) otherwise"
        )